The present disclosure provides techniques for machine learning-based anomaly prediction. A set of flight data for a flight of an aircraft is accessed, and an embedding is generated by processing the set of flight data using an autoencoder machine learning model. A reconstruction error is generated based on the embedding using the autoencoder machine learning model. An anomaly measure is generated for the set of flight data by processing the embedding and the reconstruction error using an anomaly machine learning model. In response to determining that the anomaly measure satisfies one or more criteria, an alert is output.


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    Title :

    MACHINE LEARNING FOR PREDICTIVE IN-FLIGHT ALERTS


    Contributors:

    Publication date :

    2024-08-22


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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